ArticleScientific reports2024
Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Improving In Silico Cardiac Safety Prediction by Consensus Averaging of Transmural Ventricular Cell Models.Annals of biomedical engineering · 2026Article
- AI-based discovery of functional boundaries in the human brain from intraoperative electrophysiology.medRxiv : the preprint server for health sciences · 2026Article
- Stacking Ensemble Machine Learning for Cardiac Safety Assessment Using hiPSC-CM MEA Data.Annals of biomedical engineering · 2026Article
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Validation of new AI-based classification method for in silico cardiac safety assessment of drugs following the CiPA framework.Archives of toxicology · 2025Article
- Machine Learning on Toxicogenomic Data Reveals a Strong Association Between the Induction of Drug-Metabolizing Enzymes and Centrilobular Hepatocyte Hypertrophy in Rats.International journal of molecular sciences · 2025Article
- Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy.Toxics · 2024Review
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5 authors.
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Abstract
The Comprehensive In-vitro Proarrhythmia Assay (CiPA) initiative aims to refine the assessment of drug-induced torsades de pointes (TdP) risk, utilizing computational models to predict cardiac drug toxicity. Despite advancements in machine learning applications for this purpose, the specific contribution of in-silico biomarkers to toxicity risk levels has yet to be thoroughly elucidated. This study addresses this gap by implementing explainable artificial intelligence (XAI) to illuminate the impact of individual biomarkers in drug toxicity prediction. We employed the Markov chain Monte Carlo method to generate a detailed dataset for 28 drugs, from which twelve in-silico biomarkers of 12 drugs were computed to train various machine learning models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF), XGBoost, K-Nearest Neighbors (KNN), and Radial Basis Function (RBF) networks. Our study's innovation is leveraging XAI, mainly through the SHAP (SHapley Additive exPlanations) method, to dissect and quantify the contributions of biomarkers across these models. Furthermore, the model performance was evaluated using the test set from 16 drugs. We found that the ANN model coupled with the eleven most influential in-silico biomarkers namely
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